MEDINFO 2001 V. Patel et al. (Eds) Amsterdam: IOS Press © 2001 IMIA. All rights reserved
Intelligent Systems For Nursing Education Peter Kokola, Viljem Brumecb, Ana Habjaničb, Dušanka Micetić Turkb, Paula Procterc, Linda Nicklinc a
b
University of Maribor, Smetanova 17, Slovenia Univeristy of Maribor, University College of Nursing Studies, Žitna 15, Slovenia c University of Sheffield, UK
the curriculum. However computer managed tools for nursing education Computer Managed Instruction (CMI) has been used in nursing education [12] since the late 1960s [14, 16]. It is due to the accessibility and self paced format that CMI is very well suited for both students and practicing nurses [14,16], while learning can occur at the learner's own pace and time. In addition, CMI supports also continuing education and distant learning.
Abstract Health care is one of the fastest growing areas in terms of care, treatment and the exploitation of new technology in Slovenia. There is a great need for new approaches ensuring that education and work of health care professionals will be built upon the state of the art in nursing. As a consequence the educational, governmental and “industrial” institutions from Slovenia, UK, Italy and Greece have determined to work on above problem. EU agreed to support the project under the Phare Tempus Framework and the aim of this paper is to present an educational approach based on intelligent systems and its application in nursing education.
The early applications of CMI employed room sized mainframes at large institutions, but the proliferation of microprocessors in the 1990 expanded the depth and breadth of instructional computing [10, 18]. Recent studies [7, 13, 14] show that students using CMI have better average examination scores, improved ability for critical thinking [18] and enhanced computer literacy, facilitated decision making skills and positively affected achievements [9]. In spite of these advantages still many students and faculty [15] are still reluctant to utilise CMI, but Haus [13] reports that the perception and attitude toward computer managed instruction positively changes after actual use of CMI software packages.
Keywords: artificial intelligence, informatics
intelligent
systems.,
nursing
Introduction
After the introduction of the Intranet many researcher reports about its use in nursing education. Cunningham and Plotkin [19] reports the successful use of Intranet in Nursing Clinical Practice Use and Todd [21] the very positive experiences with the use of E-mail in undergraduate teaching.
Health care is one of the fastest growing areas in terms of care, treatment and the exploitation of new technology in Slovenia. There is a great need for new approaches ensuring that education and work of health care professionals will be built upon the state of the art in nursing [3]. As a consequence the educational, governmental and “industrial” institutions from Slovenia, UK, Italy and Greece have determined to work on above problem and EU agreed to support the project under the Phare Tempus Framework [1, 6]. The aim of this paper is to present an educational approach based on intelligent systems and its application in nursing education.
Durban and Woods [7] list three benefits for using CMI and multimedia tools in the educational process: 1. quality multimedia presentation reduces the cost, in spite that initial investments are large the reduction of participants and instructors time are significant; 2. the effectiveness of the teaching and learning is improved because of greater motivation, retention, and mastery of learning;
Computers in nursing education Saranto et al. [20] argued that computers and information technology should be incorporated into all nursing curricula. However, nursing programs have varied opinions as to how this material would be incorporated, if at all, into
3. production is improved because of increased satisfaction and enjoyment of learning.
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• They must be articulate, they must effectively communicate (both what they know and what they do not) to people or other intelligent systems and explain the rationale behind their actions.
But the majority of the current teaching tools for nursing education are based on the so called concept of Drill and Practice [11], motivated by the research of Skinner [17]. The major advantage of such type of learning is the immediate feedback to a student. There is no waiting period for correction and therefore students do not practice their mistakes. But some researchers suggest that after the novelty effect of drill and practice wears off [2, 5] and the motivational power is lost. The wear effect can be overcome if the educational package is adaptive and can be individualised. This can be achieved with the use of artificial intelligence and automated learning, which in addition offers the possibility to analyse the mistakes and explain the problem to a student. Thereafter we decided to employ the concept of intelligent systems to improve the learning process in nursing education.
• They must be trainable, they must improve over time, either by learning from being told, or by capturing experience themselves. Inteligent systems in nursing Browsing trough the recent literature we found following applications of intelligent systems in nursing: • Protocols and guidelines: protocols for medical and nursing procedures and therapies, clinical guidelines, healthcare processes; • Automatic diagnosing and decision support tools: knowledge acquisition and learning, decision support theories, diagnostic problem solving, probabilistic models and fuzzy logic;
Intelligent systems A prominent researcher in the field of machine intelligence Randy Davis of MIT's AI Lab describes intelligent systems as "power tools for thinking," and draws an analogy with mechanical tools that increase our physical abilities (cranes to lift vast amounts, telescopes to see farther, etc.). Intelligent systems are power tools for heavy lifting in the information world; in Davis's words they "complement, extend, and amplify our ability to think and solve problems in a manner analogous to the way that mechanical tools complement, amplify, and extend our physical capabilities."
• Temporal Reasoning and Planing: planning and optimisation the therapies, patients management, global healthcare planning, planing environments; • Natural language and terminology: medical and nursing dictionaries, automatic abstracting, information retrieval, communication, multilingual dictionaries, lexicons • Image and signal processing: image interpretation, pattern recognition, identification;
In general we can define three different kinds of intelligent systems:
Intelligent Systems Approaches
• autonomous systems that can plan and initiate actions in the real world, such as assembly-line robots,
Various approaches enabling to built intelligent systems according to above requirements and belonging to above classes do exist. Some of the most known are:
• associate systems that collaborate with a person to make decisions, such as intelligent nurse workstations, and
1. Neural networks 2. Evolutionary algorithms and programming
• advisory systems that deliver information to support a person's decision making, such as a voicecontrolled airline-schedule advisor.
3. Decision trees 4. Rough sets 5. Hybrid systems, approaches.
On the surface these systems differ in the degree of authority granted for action in the real world, the degree and type of co-operation with people, the type of intelligent assistance given, the degree of naturalistic intelligence in communication and co-operation, and the richness with which the systems acquire information from the world. However, at a very high level they share certain characteristics:
combining
some
of
above
It is our belief that from above approaches the decision trees are the best, because of their simplicity, two dimensional structure and non black – box manner.
Decision trees
• They must be taskable in a natural way, at some level, people will be in control and must interact with these systems naturally and effortlessly.
The algorithm for learning a decision tree is trivial and the representation of accumulated knowledge can be easily understood. Namely, the decision trees do not give us just the decision in a previously unseen case - they also give us the explanation of the decision, and that is essential in educational settings.
• They must be competent, they must represent and reason with domain knowledge as well as general knowledge about the world, the user, and the task.
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whole database and select a diagnosis (output) in our example if an infant is still breastfeed at the age of 6months and if it already get some additives. Combining these two diagnoses we get three different classes: breastfeed, partially breastfeed and not breastfeed. Following decision tree has been generated.
A decision tree is induced on a training set, which consists of training objects (instances). Each training object is completely described by a set of attributes and a class label (category, outcome). Classes are mutually exclusive, what means that the training object can belong to only one class. Attributes can be continuous (numeric) or discrete. Continuous attributes are not suitable for learning a tree, so they must be mapped into a discrete space. A decision tree contains nodes and edges (links). There are two types of nodes. Each internal node (non-terminal node) has a split, which tests the value of the chosen attribute for the training objects, that have come into this node and according to that splits the training set. Each internal node has at least two child nodes. External nodes, also called leaves or terminal nodes, are labelled with outcomes. Nodes (internal and external) are connected with edges. Edges are labelled with different outcomes of test, performed in the source node. Number of edges that come out of the node depends on the number of possible outcomes of the test.
[] type of additional feeding by age of 2 months? |__[none] BREASTFEEDED |__[bottle] mother is employed? | |__[YES] number of meals? | | |__[5..6.75] using dummy? | | | |__[yes] head circumference? | | | | |__[31..32.75] PARTIALY BREASTFED | | | | |__[32.75..34.5] NOT BREASTFED | | | | |__[34.5..36.25] PARTIALY BREASTFED | | | | |__[36.25..38] NOT BREATFED | | | |__[no] weight by delivery? | | | |__[2650..3032.5] BREASTFED | | | |__[3032.5..3415] PARTIALY BREASTFED | | | |__[3415..3797.5] BREASTFED | | |__[6.75..8.5] weight by delivery? | | | |__[2650..3032.5] BREASTFED | | | |__[3032.5..3415] type of additional feeding by age of 4 months? | | | | |__[bottle] NOT BREASTFED | | | | |__[bootle or spoon] education of mother? | | | | | |__[10.25..12.5] NOT BREASTFED | | | | | |__[12.5..14.75] BREASTFED | | | | |__[sponn] BREASTFED | | | |__[3415..3797.5] NOT BREASTFED | | | |__[3797.5..41] NOT BREASTFED | | |__[8.5..10.25] NOT BREASTFED | |__[no] NOT BREASTFED | |__[student] BREASTFED |__[bootle or spoon] BREASTFED |__[spoon] NOT BREASTFED
Unlike some other approaches the representation of a decision tree can be easily understood by a human. All tests in internal nodes of a tree can be determined so the importance of attributes can be obtained from the decision tree. This that is the way to take advantage of the decision trees even without using them for their primary task in decision making. According to above, decision trees can support the nursing education process in four ways: 1. to represent the knowledge and decision making as a simple two-dimensional hierarchical model; 2. to outline important factors needed for successful decision making
Figure 1 – Sample decision tree
3. to enable a nurse to use the decision tree (in the paper form or as a computer program) to learn, support and test their own decision making in new situations
First thing that a midwife can learn from such a decision tree is which attributes mostly influence the diagnosis. In our case these are:
4. using their own databases to construct the decision tree (using automatic learning) for their own cases.
• type of additional feeding by age of two months? • mother is employed?
5. add or omit various attributes in the database and learn how these changes influence the knowledge representation and the decision making process
• number of meals? • using dummy?
6. change the attribute values and learn how these changes influence the knowledge representation and the decision making process.
• head circumference? • weight by delivery?
Sample decision tree – Breast feeding
• education of mother?
Knowledge about breast feeding is very important for midwifes in the manner that they can teach mothers i. e. which factors (attributes) do influence the duration and success of breast feeding. To find that out a midwife can generate a decision tree from a breastfeeding database (In Slovenia such a database is being created by the project INSIST), selecting various attributes, various diagnosing attributes, etc. In the case that the midwife is not very knowledgeable about breastfeeding she/he can select the
The next interesting thing is a decision process itself. For example from the tree we can read: If the type of additional feeding by age of two months? is none then the infant will be breastfed by 6 month
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Amsterdam, The Nederlands, April 12-14, 1999: proceedings,(Lecture notes in computer science, 1593). Berlin [etc.]: Springer. [6] Kokol P, et al, 2000, ODIN – On Demand Intranet for Nursing Education, Proceedings ITIS/ITAB conference, Washington, IEEE Press. [7] Athappilly KK, Durben C, Woods S, 1994, Multimedia Computing., Reisman, Harrisburg. [8] Ball M. J. 1995, Nursing Informatics. Springer, New York. [9] Belfry J M, 1988 A Review of the Effect of Computer Assisted Instruction in Nursing Education, Computers in Nursing, March. [10] Cambre M, L. J. Castner, J 1993 The status of interactive video in nursing education environments, FITNE, Atlanta. [11] Conrick M. 1998 Computer Based Education: more than just a package. Australian Electronic Journal of Nursing Education 4:1 October. [12] Habjanič, A , Kokol P, Zorman M, Japelj, M, 1998 CArE: A software package for computer aided nurse education, unpublished paper. [13] Haus, K, 1996 Changes in Perception of Computer Aided Instruction, Research report, (HYPERLINK http://parsons.ab.umd.cdu/~khaus/paper.html http://parsons.ab.umd.cdu/~khaus/paper.html) [14] Hebda T, 1998 A Profile of the Use of CAI within baccalaureate nursing education, Computers in Nursing, November. [15] Khoiny, F. 1995 Factors that contribute to computer assisted instruction effectiveness, Computers in Nursing, July. [16] Kohl J E, 1995. Computer Assisted Instruction: Implications for Achievement and Critical Thinking, Research report, (http://www.nursing.ab.umd.edu/students/~jkohl/cai.ht). [17] Skinner, B. 1953 Science of Human Behaviour. New York, MacMillan. [18] Williamson M, 1994 High tech training, Byte, December [19] Helene Cunningham, Karen Plotkin Using the Internet in a Nursing Clinical Practicum Course: Benefits and Challenges, AEJNE Volume 5 - No.2 March, 2000. [20] Saranto K, Leino-Kilpi H, Isoaho H 1997 Learning environment in information technology: The views of students. Computers in Nursing 15(6): 324-332 [21] Todd N 1998 Using E-mail in an undergraduate nursing course to increase critical thinking skill. Computers in Nursing 16(2): 115-118
what is quit an interesting finding that instructs a midwife that she has to suggest mothers not to give any additives to infants till 2 months. A more complicated decision process is If the type of additional feeding by age of two months? is bottle and mother is employed? is yes then the infant is not breastfed. or If the type of additional feeding by age of two months? is bottle and mother is student? is yes then the infant is breastfed. Above finding seems very logical, but sometimes such straightforward conclusions are not directly evident nor from experience nor form the database. Many other decision more complicated decisions can be learned from the above decision tree, also the midwife can select different diagnosis to find out additional knowledge form the database, etc.
Conclusion The aim of the ODIN project is to improve and better support nursing education with the use of information technology. In this paper we employed artificial intelligence, more specifically machine learning and decision trees. It is our belief that in such way the use of computers in nursing education can become still more successful resulting in an overall health care process improvement.
Acknowledgement The work reported in this paper is partially sponsored by the EU Phare Tempus Project ODIN and the Slovenian government project INSIST.
References [1] Kokol. P et al. Tempus ODIN proposal. 1999. [2] Kokol P., Zorman M., Medos T. Decision trees - a CMI tool in nursing education. Aust. electron. j. nurs. educ., 1999, 4(2). [3] Kokol P. et al,. Nursing informatics education for the next millenium. FGCS, Future gener. comput. syst., 1999, 15(2) 211-216. [4] Habjanic A. et al,. CArE: a software package for computer-aided nurse education. Health inform. j., 1999, 5(3) 119-123. [5] Kokol, P, et al, 1999 Decision trees - a CIM tool in nursing education. V: SLOOT, Peter (ur.). Highperformance computing and networking :7th international conference, HPCN Europe 1999
Adress for correspondence Peter Kokol University of Maribor Faculty for Engeeniring, Electrotechnics and Informatics Smetanova 17, 2000 Maribor, Slovenia
[email protected]
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